In this study, a multi-frequency statistical algorithm is proposed for retrieving surface soil temperature (T-s) from AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation System) brightness temperature (T-B) observations. The algorithm was developed based on a regression analysis between T-B from all AMSR-E bands and the corresponding in situ T-s observed by a soil moisture and temperature network in the central Tibetan Plateau. The new algorithm effectively utilizes information from the different bands provided by AMSR-E, lessening the influence of soil moisture, vegetation, and water vapour. Further validations were conducted based on seven soil moisture and temperature observation networks distributed globally. The results showed that the new multi-frequency algorithm can produce T-s values with a mean bias of less than 2 K and a root mean square error of less than 3 K for different vegetation-covered areas of the globe. Compared with a widely used single-band inversion algorithm, the new multi-frequency algorithm has better accuracy in estimating T-s and does not suffer from considerable overestimation or underestimation across these networks, indicating good transferability. This algorithm could contribute to research relating to the land energy balance by providing consistent and independent long-term estimates of daily global T-s. Nevertheless, the new algorithm has limited ability to retrieve T-s of frozen soil, given that AMSR-E T-B values are affected by the deep soil temperature after a soil is frozen.

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Page number:

6735-6754

Issue:

23

Subject:

Authors units:

PubYear:

2017

Volume:

38

Publication name:

International Journal of Remote Sensing

Abstract:

In this study, a multi-frequency statistical algorithm is proposed for retrieving surface soil temperature (T-s) from AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation System) brightness temperature (T-B) observations. The algorithm was developed based on a regression analysis between T-B from all AMSR-E bands and the corresponding in situ T-s observed by a soil moisture and temperature network in the central Tibetan Plateau. The new algorithm effectively utilizes information from the different bands provided by AMSR-E, lessening the influence of soil moisture, vegetation, and water vapour. Further validations were conducted based on seven soil moisture and temperature observation networks distributed globally. The results showed that the new multi-frequency algorithm can produce T-s values with a mean bias of less than 2 K and a root mean square error of less than 3 K for different vegetation-covered areas of the globe. Compared with a widely used single-band inversion algorithm, the new multi-frequency algorithm has better accuracy in estimating T-s and does not suffer from considerable overestimation or underestimation across these networks, indicating good transferability. This algorithm could contribute to research relating to the land energy balance by providing consistent and independent long-term estimates of daily global T-s. Nevertheless, the new algorithm has limited ability to retrieve T-s of frozen soil, given that AMSR-E T-B values are affected by the deep soil temperature after a soil is frozen.